Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.

Area of Science:

• Musculoskeletal imaging
• Radiology
• Artificial Intelligence in Medicine

Background:

• Osteoarthritis (OA) is a heterogeneous joint disease affecting cartilage, meniscus, ligaments, and tendons, often characterized by short T2 relaxation times.
• Early detection of OA is critical to prevent irreversible changes and manage the growing disease burden.
• Advanced imaging and deep learning (DL) offer potential for visualizing and analyzing early OA changes, termed "pre-OA".

Purpose of the Study:

• To review ultrashort echo time-based MRI techniques for assessing short- and long-T2 musculoskeletal tissues.
• To explore the role of deep learning (DL) in revolutionizing OA MRI analysis, including segmentation and biomarker extraction.
• To highlight the combined potential of these technologies in identifying imaging biomarkers for pre-OA.

Main Methods:

• Review of ultrashort echo time (US রোগীর) magnetic resonance imaging (MRI) techniques for musculoskeletal tissues.
• Exploration of deep learning (DL) algorithms for MRI analysis in OA.
• Focus on quantitative morphological and compositional assessment of joint tissues.

Main Results:

• Ultrashort echo time-MRI enables direct visualization and quantitative assessment of short-T2 tissues.
• Deep learning significantly advances MRI analysis for OA detection, classification, and prediction.
• Combined approaches show progress in identifying imaging biomarkers for pre-OA.

Conclusions:

• Ultrashort echo time-MRI and deep learning represent significant advancements in musculoskeletal imaging for OA.
• These technologies facilitate the identification of imaging biomarkers for early OA detection and management.
• The integration of advanced MRI and DL holds promise for proactive OA care.